This work reports the importance of nanopore morphology in designing super liquid-repellent submicropillar/ nanopore hierarchical surfaces. The hierarchical surfaces were fabricated using a combined process of oblique angle sputter deposition of aluminum with subsequent anodizing, and the surfaces were coated with a fluorinated alkyl phosphate layer to reduce the surface energy. The size of the nanopores, the interpore distance, and the porosity of the anodic films on the submicrometer pillars were controlled by varying the anodizing and pore-widening conditions. The present study demonstrates that super liquid repellency can be achieved on intrinsically oleophilic surfaces by introducing hierarchical submicropillar/nanopore morphology even for oils with surface energies as low as ∼25 mN m −1 . The porosity in the submicrometer pillars was a key factor in influencing the contact angle hysteresis; higher porosity is needed to reduce the contact angle hysteresis.

Self-assembled alkyl phosphate layers have been formed on a flat, anodized aluminum substrate in dilute ethanol solution containing 2 wt% n-tetradecylphosphonic acid (TDP) and examined by low-voltage scanning electron microscopy as well as atomic force microscopy and X-ray photoelectron spectroscopy. Locally, multi-layered alkyl phosphate films have been formed on aluminum, being clearly observed by a low-voltage scanning electron microscope operated at less than 1 kV. Atomic force microscopy observations disclosed that bilayers of tetradecylphosphonic acid are stacked on the substrate to form multilayers. The present study revealed that the uniform self-organized monolayer is not always formed readily on an oxidized aluminum surface.

Purpose This paper aims to present a deep learning–based surrogate model for fast multi-material topology optimization of an interior permanent magnet (IPM) motor. The multi-material topology optimization based on genetic algorithm needs large computational burden because of execution of finite element (FE) analysis for many times. To overcome this difficulty, a convolutional neural network (CNN) is adopted to predict the motor performance from the cross-sectional motor image and reduce the number of FE analysis. Design/methodology/approach To predict the average torque of an IPM motor, CNN is used as a surrogate model. From the input cross-sectional motor image, CNN infers dq-inductance and magnet flux to compute the average torque. It is shown that the average torque for any current phase angle can be predicted by this approach, which allows the maximization of the average torque by changing the current phase angle. The individuals in the multi-material topology optimization are evaluated by the trained CNN, and the limited individuals with higher potentials are evaluated by finite element method. Findings It is shown that the proposed method doubles the computing speed of the multi-material topology optimization without loss of search ability. In addition, the optimized motor obtained by the proposed method followed by simplification for manufacturing is shown to have higher average torque than a reference model. Originality/value This paper proposes a novel method based on deep learning for fast multi-material topology optimization considering the current phase angle.

No abstract

No abstract